Recently with the widespread emergence of personal navigation devices (GPSs), mashups created with Google maps, location-based services, and new mobile devices, large quantities of time-stamped geoencoded data have become available for analysis. Time-stamped geoencoded data are quite common and include, for example, the sequence of waypoints generated by a GPS, time-stamped leaks along a gas pipeline, the scheduled deliveries and their times of a delivery vehicle, measurements of energy usage from household thermostats through the energy distribution network back to the power plants, the positions of miners with active RFID badges in a large underground mine, customer purchases through time geoencoded by customer address, and even item sales encoded with aisle, shelf, and position within a retail store. This class of data is fairly common and involves sequences of events in time with various spatial types of spatial constraints on the positions. For example, vehicle-based GPS positions will (for the most part) be constrained to be on road or at known addresses. Items in a store are normally on shelves positioned in aisles.
One way to display and analyze time-stamped geoencoded data is to create a mashup using Google's or Microsoft's web-based mapping applications. Unfortunately, these platforms do not permit deep time-based analysis. The problem is that these web mapping applications are intended to show positions on a map, to support searching, to provide directions, and to serve up local ads. Both Google's and Microsoft's sites are advertising supported. Thus the APIs for these applications are optimized to serve up relevant ads. Additionally, the content in these images is static. It is not possible to generate a custom image tile that combines real-time information with static content using Google, Microsoft or others' web-based mapping API. Using these platforms, it is not possible to correlate geospatial information by time, by event type, by trend, etc., to discover important analytical relationships.
Traditional geographical information system (GIS) systems are also not designed for web-based analysis of time-oriented geospatial business intelligence data. These applications are often overwhelming complex, run on high-end desktop workstations, require specialized programming experience to create/modify and are optimized to analyze geospatial layers. However, computing is in the midst of a massive change as the traditional desktop systems are moving to a server-based computation model where desktop software is being replaced by rich web 2.0 browser-based interfaces delivered to mobile devices. What is needed for most users are lightweight, web-based tools that enable time-stamped geospatial information to be encoded and analyzed within a web page on a spatial canvas, for example, a traditional Google or Microsoft map or a nontraditional map such as a floor plan, layout of a coal mine, or even a multistory building.